U.S. patent number 7,873,478 [Application Number 11/195,365] was granted by the patent office on 2011-01-18 for mass spectrometric differentiation of tissue states.
This patent grant is currently assigned to Bruker Daltonik GmbH. Invention is credited to Jochen Franzen, Martin Schurenberg, Detlev Suckau.
United States Patent |
7,873,478 |
Suckau , et al. |
January 18, 2011 |
Mass spectrometric differentiation of tissue states
Abstract
The invention relates to the determination and visualization of
the spatial distribution of tissue states in histologic tissue
sections on the basis of mass spectrometric signals acquired so as
to be spatially resolved. The invention provides a method which
determines the tissue state for the tissue spots as a state
characteristic, which is calculated as a mathematical or logical
expression from at least two mass signals of this tissue spot, and
which indicates the tissue state as a gray-level or false-color
image in one or two dimensions.
Inventors: |
Suckau; Detlev (Grasberg,
DE), Schurenberg; Martin (Tarmstedt, DE),
Franzen; Jochen (Bremen, DE) |
Assignee: |
Bruker Daltonik GmbH (Bremen,
DE)
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Family
ID: |
34983970 |
Appl.
No.: |
11/195,365 |
Filed: |
August 2, 2005 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20060063145 A1 |
Mar 23, 2006 |
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Foreign Application Priority Data
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Aug 3, 2004 [DE] |
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10 2004 037 512 |
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Current U.S.
Class: |
702/19; 436/173;
250/287; 250/281 |
Current CPC
Class: |
G01N
33/483 (20130101); Y10T 436/24 (20150115); H01J
49/0004 (20130101) |
Current International
Class: |
G06F
19/00 (20060101); G01N 24/00 (20060101); H01J
49/00 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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WO 03/034024 |
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Apr 2003 |
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WO |
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WO 03/104794 |
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Dec 2003 |
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WO |
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Other References
Stoeckli et al. (Nature Medicine (2001) vol. 7, No. 4, pp.
493-496). cited by examiner .
Luxembourg, et al., "Effect of Local Matrix Crystal Variations in
Matrix--Assisted Ionization Techniques for Mass Spectrometry",
Anal. Chem., vol. 75, pp. 2333-2341, American Chemical Society,
2003. cited by other .
Stoeckli, et al., "Molecular imaging of amyloid .beta. peptides in
mouse brain sections using mass spectrometry", Analytical
Biochemistry, vol. 311, pp. 33-39, Elsevier Science, 2002. cited by
other .
Pusch, et al., "Mass spectrometry-based clinical proteomics",
Pharmacongenomics, vol. 4, pp. 463-476, Ashley Publications Ltd.,
2003. cited by other .
Stoeckli, et al., "Molecular Imaging of Amyloid .beta. Peptides in
Mouse Brain Sections Using Mass Spectrometry", Analytical
Biochemistry 311, 2002, pp. 33-39, Academic Press. cited by
other.
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Primary Examiner: Clow; Lori A
Attorney, Agent or Firm: Law Offices of Paul E. Kudirka
Claims
The invention claimed is:
1. A method for the detection and visualization of the spatial
distribution of tissue states of a tissue section comprising: a)
preparing the tissue section for a mass spectrometric analysis on a
support; b) at each of a plurality of localized spots physically
spaced apart along one or two dimensions of the tissue section,
performing a measurement with a mass spectrometer that produces a
primary ion mass spectrum with a plurality of mass signals for that
spot; c) at each of the plurality of spots, combining at least two
different mass signals of the plurality of mass signals produced at
that spot, the two different mass signals representing two
different substances, with predetermined mathematical or logical
expressions to generate a combination value that represents a
tissue characteristic at that spot; and d) displaying the
combination values generated in step (c) at the corresponding
physical spot positions to provide a visualization of the spatial
distribution of tissue states of the tissue section.
2. The method according to claim 1, wherein step (d) comprises
displaying the combination values with brightness levels or false
colors.
3. The method according to claim 1 further comprising superimposing
a microscopic image of the tissue section true to position under
the display of the spatial distribution of the tissue states.
4. The method according to claim 3, wherein the microscopic image
of the tissue section is represented by different color densities
and the spatial distribution of the tissue states is represented by
shades of color.
5. The method according to claim 1, wherein the mathematical or
logical expressions used in step (c) are obtained from previous
mathematical analyses of large numbers of sample measurements.
6. The method according to claim 1, wherein the mathematical or
logical expressions used in step (c) are obtained from mathematical
analyses of the mass spectrometric signals of two defined regions
of the tissue section.
7. The method according to claim 6, wherein the two regions for the
mathematical analyses of mass spectrometric signals are defined by
reproducing the image of the tissue section on a computer
screen.
8. The method according to claim 1 further comprising preparing the
tissue sample for the mass spectrometric analyses by transferring
substances from the tissue section onto a copy medium, and
performing step (b) on the surface of the copy medium.
9. The method according to claim 8, wherein the copy medium is a
blot membrane.
10. The method according to claim 8, wherein the copy medium is a
surface coated with antibodies.
11. The method according to claim 1, wherein step (b) comprises
ionizing portions of the tissue section by matrix-assisted laser
desorption.
12. The method according to claim 1, wherein steps (a)-(c) are
performed on each of several microscopic tissue areas which are all
part of a single tissue sample and, in step (d), the tissue states
are represented three-dimensionally.
Description
FIELD OF THE INVENTION
The invention relates to the determination and visualization of the
spatial distribution of tissue states in histologic tissue sections
on the basis of spatially resolved mass spectrometric signals.
BACKGROUND OF THE INVENTION
The term "tissue state" here means the state of a small subarea of
a tissue section with respect to a stress, a pathological change,
an infection or other type of change compared with a normal state
of this tissue. The tissue state must therefore be identifiable as
a concentration pattern of substances which can be detected in this
small subarea by a mass spectrometer. The substances can be
peptides or proteins which are under- or overexpressed and hence
form a pattern, or they can include positranslational modifications
of proteins, their breakdown products (metabolites), or collections
of other substances in the tissue.
Mass spectrometry with ionization of the samples by matrix-assisted
laser desorption and ionization (MALDI) has been used successfully
for several years for the determination of molecular weights, and
for the identification and structural characterization of proteins.
In this case, the protein is usually dissolved and mixed with a
solution of a matrix substance such as sinapic acid before being
applied to the sample support. The solvent then evaporates and the
matrix substance crystallizes, the protein crystallizing with it in
the matrix crystals. Bombarding the sample obtained in this way
with sufficiently energetic short pulses of laser light leads to
the matrix substance absorbing energy and evaporating explosively
as a result. The proteins are entrained into the gaseous cloud
inside the mass spectrometer and ionized by protonation. The ions
are then separated in the mass spectrometer according to their
mass-to-charge ratios (m/z) and measured as a mass spectrum. Their
mass can be determined from the mass spectrum. Since ionization by
matrix-assisted laser desorption essentially provides only singly
charged ions, in the following, we will simply refer to "mass
determination" and not determination of the mass-to-charge ratios
and, correspondingly, just the "mass" of the ions instead of their
m/z-ratio.
These analyses can be carried out on biological samples, such as
tissue homogenates, lyzed bacteria or biological fluids (urine,
blood serum, lymph, spinal fluid, tears, sputum), the samples
generally being subjected to sufficient fractionation beforehand by
chromatographic or electrophoretic techniques.
For this purpose it is advisable to free the samples from
interfering impurities, such as certain buffers, salts or
detergents, which reduce the efficiency of the MALDI analyses. The
analysis of biological samples usually involves very time-consuming
sample preparation, particularly if, at the same time, information
concerning the distribution of a protein in different regions of a
tissue is to be obtained. "Laser capture microdissection", for
example, can achieve this, but the time-consuming processing
described above is still necessary; there is also the difficulty of
obtaining sufficient material for this type of analysis.
Imaging mass spectrometry (IMS) makes it unnecessary to go to these
lengths. With this method, a microscopic tissue section is produced
from a piece of tissue taken from a human or animal organ of
interest using a microtome, for example, and laid on a specimen
slide. A matrix capable of absorbing laser energy is then applied
to the surface of the specimen, for example by pneumatic spraying
onto a moving support (U.S. Pat. No. 5,770,272; Biemann et al.).
There are two different methods for the subsequent mass
spectrometric scan: The raster scan method and stigmatic imaging of
the ions of a small region.
The raster scan method produces a one- or two-dimensional intensity
profile for individual proteins by scanning a microscopic tissue
section with well-focused laser beam pulses in a MALDI mass
spectrometer, the proteins being identifiable in the mass spectra
(U.S. Pat. No. 5,808,300; Caprioli). Each spot is therefore
irradiated at least once with a finely focused pulse of laser light
and provides a mass spectrum which can cover a broad range of
molecular weights, for example 1 to 30 kilodaltons. Using suitable
software, it is then possible to define an ion mass, which
represents a peptide or a protein, or a narrow mass range around
this mass, in the spectra and to graphically represent its
intensity distribution over the surface of the microscopic tissue
section. Using this method, it has been possible to correlate the
distribution of neuropeptides in the brain of a rat with specific
morphological features, for example, or to depict the distribution
of amyloid beta peptides in the brains of Alzheimer animal models.
It is possible to visualize sections of the brain affected by
"Alzheimer plagues" with precise spatial definition (Stoeckli M,
Staab D, Staufenbiel M, Wiederhold K H, Signor L, Anal Biochem.
2002, 311, 33-39: Molecular imaging of amyloid beta peptides in
mouse brain sections using mass spectrometry).
The method of stigmatic imaging irradiates a defined area of up to
200 by 200 micrometers with the laser pulse. The ions formed over
the area are imaged ion-optically, spot by spot on a
spatially-resolving detector. So far, it has been possible to scan
distribution images of these ion masses by selecting individual ion
masses with this method (S. L. Luxembourg et al., Anal. Chem. 2003;
75, 1333-41); it is to be expected, however, that very fast cameras
will be able to scan complete mass spectra for every spot of the
area.
A considerable disadvantage of both methods is the fact that, until
now, only individual features in these types of spectra have been
utilized analytically, for example a peptide present in a high
concentration, which is particularly typical of certain tissue
states within a tissue sample. This procedure has limited the
method until now and prevented a broader application for those
tissue states which cannot be attributed to the appearance of one
single peptide or protein.
Independently of such imaging methods, targeted searching for
"markers" has developed as an interesting field of clinically
oriented research (W. Pusch et al., Pharmacogenetics 2003; 4,
463-476). Here, bodily fluids such as blood, urine or spinal fluid,
but also tissue extracts, are typically processed into coarse
fractions with a less complex analyte composition by extracting
them with- chromatographic phases, solid phase extraction or other
selective methods before they are mass spectrometrically
characterized. The mass spectra obtained by this method display a
more or less complex pattern which originates from peptides and
proteins. By comparing the mass spectra of samples from healthy and
sick individuals it is possible, in individual cases, to find
single peptides or proteins which are characteristic of the medical
condition of the individuals.
However, there is a general opinion that interesting distinguishing
features with better statistical evidence can only be discovered
when this method is performed on dozens or hundreds of samples from
two so-called cohorts of individuals--one cohort serving as a
reference and one cohort in which certain peculiarities or
deviations in the spectra are expected because a specific clinical
picture, such as intestinal cancer or prostate cancer, is
present.
This approach has achieved preliminary successes with the discovery
of distinct and statistically significant protein signals in the
case of samples from ill persons. In the literature, however, a
vehement argument is in progress about whether these markers can be
used for diagnosis or not since, as yet, it has not been possible
to establish whether these markers might simply be indicative of
the patient's type of medication or a general stress situation
associated with the illness. For the licensing of such markers for
general diagnostic purposes, the United States FDA (Food and Drug
Administration) now requires, as a minimum, that the protein found
as a marker is unambiguously identified and that knowledge of the
protein and its function (or its breakdown pathway, if the
substance in question is a breakdown product) is used to at least
establish the plausibility of a link with the illness
concerned.
The objective of these analyses is naturally to make an early
prediction about the possible development or proliferation of
various diseases in the future of an individual. It is hoped that
it will be possible to identify cancer at a very early stage, for
example, and therefore to have a much better chance of fighting
it.
In general, however, the mass spectra of the various cohorts do not
contain any simple features such as a few individual signals whose
intensities differ significantly in the cohorts. Complex
mathematical-statistical analyses of the mass spectra of the
various cohorts must therefore usually be carried out. These
analyses can be carried out using a plurality of methods, which
analyze whether it is possible to distinguish between the cohorts
of healthy patients and sick patients unambiguously and to a
statistically significant degree on the basis of groups of features
in the mass spectra.
It is, for example, possible for a principal component analysis
(PCA) to determine whether cohorts of sick individuals (or, where
possible, even several cohorts with several related diseases) can
be distinguished from each other and from cohorts of healthy
reference individuals. If this is the case, a further mathematical
computational method can use the mass spectrometric signals to
calculate disease-specific distinguishing characteristics which
make it possible to unambiguously identify the state of an
individual with respect to a specific disease. Suitable
mathematical transformations can, for example, make it possible for
the disease-specific distinguishing characteristics to cover the
range from minus infinity to plus infinity, for example, where all
values less than zero correspond to a healthy state and all values
greater than zero to a diseased one. A very simple distinguishing
characteristic can be a simple concentration ratio of two proteins,
for example, where the range extends from zero to infinity.
Alternatively, the distinguishing characteristics are transformed
in such a way that they cover the range from zero to one: healthy
state close to zero, diseased state close to one. The detailed
computational method for calculating the distinguishing
characteristics (both the algorithm and the parameter values) is
saved and later used for the diagnosis of this disease using mass
spectra scanned from this individual's samples.
Genetic algorithms (GA) generate a decision path along which the
medical condition of an individual can be determined. A logical
expression can be obtained from the decision path which, in turn,
can be transformed into a characteristic which distinguishes
between different states. This logical computational method is also
saved and later used for diagnosis of other samples.
Other methods for analyzing the differentiation have been
elucidated, including: linear discriminance analysis (LDA), support
vector machines (SVM), neuronal networks (NN), learning vector
quantification (LVQ).
From the results of such statistical analyses, it is ultimately
possible to obtain detailed computational methods (algorithms plus
parameter sets) to calculate distinguishing characteristics that
are represented as mathematical or logical expressions, each
incorporating several spectra signals. These can also include very
weak spectral signals. The distinguishing characteristics also seem
to make it possible to represent more subtle differences between
samples from different cohorts. However, the number of samples
required easily runs into thousands.
It is a considerable problem here that the variation in the ion
signals in the individual mass spectra, even within one cohort
(patient or healthy), is large, and, for example, the age
distribution in a group or the gender-specific distribution can
have much more influence than the effect which is to be
investigated. One of the reasons for this is the fact that the
analysis of bodily fluids only provides a remote--or
indirect--picture of the occurrence of the disease at the site of
action (for example the tumor or the brain in the case of
neuro-degenerative diseases). According to present expectations,
the problem of the search for markers could be simplified if it
were possible to compare healthy and diseased samples from a single
individual. But this is not possible when the samples are bodily
fluids because of their homogenization in the body, and can, at
best, be determined as a temporal variation over relatively long
periods of time.
SUMMARY OF THE INVENTION
The invention provides a method which, on the one hand, can provide
a visual representation of the mass spectrometric differentiation
of tissue states and, on the other, can formulate characteristics
which distinguish between healthy and diseased tissue sections
using spatially resolved mass spectrometric signals of the analyzed
tissue, and can do this more easily than is the case with samples
analyzed on a cohort basis.
The invention first provides a method which is suitable for
visualizing the spatial distribution of tissue states of histologic
samples. It comprises the following steps: a) production of at
least one histologic sample as a tissue section, b) preparation of
the samples for mass spectrometric analyses, c) spatially
distributed detection of mass spectrometric signals along one or
two dimensions of the samples, d) calculation of localized
characteristics which distinguish between different states from at
least two mass spectrometric signals, and e) graphic representation
of the spatial distribution of these distinguishing characteristics
for at least one of the samples.
It is advisable to carry out the calculation on a computer and the
tissue imaging on a screen. This makes it possible to lay a
microscopic image of the tissue section (or tissue sections) true
to position under the image of the distinguishing characteristics.
For this, the microscopic image is represented as a color density
image (brightness), for example, and the distinguishing
characteristics as shades of false color (example: blue tissue is
healthy; red is diseased).
For this purpose, the calculation of the localized distinguishing
characteristics can utilize detailed computational methods
(algorithms and parameter sets) which have been previously obtained
from cohorts of healthy and diseased tissue homogenates.
However, since, as shown above, these computational methods are
based on widely varying samples from different individuals and can
therefore impair clear detectability, a further embodiment of the
invention will focus on the differences between healthy and
diseased tissue of the same individual. It can be expected that the
direct analysis of a tumor tissue and the surrounding healthy
tissue in the sample from an individual could reveal differences of
far greater specificity between healthy and diseased tissue. Thus,
in the image of one or more pieces of tissue on the screen, regions
can be indicated which are considered as healthy or diseased. From
the mass spectra of these regions it is possible to independently
develop (on the computer, by means of predetermined development
procedures) computational methods for the characteristics which
distinguish between different states. These can then be applied to
all spots of the tissue. The distinguishing characteristics are
then displayed in the image of the tissue. The computational method
can follow a previously determined algorithm, for example, where
only the parameter set is optimized. So-called "supervised
learning" is one such possibility.
Furthermore, the spatially resolved mass spectra can also be
scanned from a copy of the sample, i.e. not from the tissue itself.
The peptides or proteins of a tissue section can be transferred
onto a blot membrane, for example. Alternatively, they can be
transferred onto a surface which is coated with one or more types
of antibodies. It is thus also possible to display the spatial
distribution of peptides or proteins at very low concentrations.
The tissue state characteristics can thus be extended to ratio
differences of posttranslational modifications such as
phosphorylations or glycosylations, or to breakdown forms of
proteins.
BRIEF DESCRIPTION OF THE DRAWINGS
The above and further advantages of the invention may be better
understood by referring to the following description in conjunction
with the accompanying drawings in which:
FIG. 1A shows a schematic microscopic section of a mouse brain in
which two circular areas are defined whose mass spectra, scanned on
a spot-by-spot basis, are used to develop the calculation method
for the characteristics which distinguish between different states;
and
FIG. 1B shows a section like that of FIG. 1A with a characteristic
distribution over the whole tissue section.
DETAILED DESCRIPTION
One preferred embodiment begins with the production of a
microscopic tissue section, preferably from a deep-frozen piece of
tissue, with a microtome. The microscopic tissue section is applied
to a suitable support. This support can be a glass specimen slide,
for example, whose surface is equipped with a transparent but
conductive surface coating for subsequent use in the mass
spectrometer. Other supports, for example metal supports or
supports made of electrically conductive plastic, can also be used,
however. The microscopic tissue section can then be stained in the
usual way, although care has to be taken to use a stain which does
not interfere with a subsequent mass spectrometric analysis of the
tissue constituents. Fluorescence dyeing methods can also be used
if they do not restrict the mass spectrometric analysis.
After this, a microscopic image is taken, with transmitted or
reflected light, from the microscopic tissue section, and is later
used to lay under the result images. Before the image is taken,
markings which are recognizable both optically as well as mass
spectrometrically can preferably be applied to the support to
facilitate subsequent adjustment so as to obtain a true position.
Many mass spectrometers are equipped with a viewing unit for the
samples, which can likewise be used for the true-to-position
adjustment.
The microscopic tissue section is then sprayed with a solution of a
suitable matrix substance for ionization by matrix-assisted laser
desorption. The spraying can be done on a device which moves the
specimen slide under the spray jet so that a uniform sprayed layer
is achieved, for example. Care must be taken to ensure that the
positional accuracy of the samples is not adversely affected by the
sprayed liquid running. During this process, the matrix substance
which is crystallizing out absorbs such substances from the
microscopic section as can be integrated into the microcrystals
themselves or into grain boundaries between the microcrystals
during the crystallization.
The choice of matrix substance can greatly influence which
biomolecules in the spectra lead to signals. Proteins are prepared
for MALDI MS analysis with 2.5 dihydroxybenzoic acid (DHB) or
sinapic acid (SA), for example; peptides with
.alpha.-cyano-4-hydroxycinnamic acid (CCA), nucleic acids with
3-hydroxypicolinic acid (3-HPA) and saccharide-carrying structures
with DHB or trihydroxyacetophenone.
In another similarly favorable embodiment, spatially resolved mass
spectrometry can be carried out on a copy rather than on the
original tissue section. It is thus possible, for example, to bring
the moist microscopic tissue section into contact with a blot
membrane either before or after the microscopic image is taken.
Blot membranes are known from two-dimensional gel electrophoresis;
they can bind proteins and peptides by their affinity in a
particular way so that they are stationary. The substances can be
transferred onto the blot membrane by simple diffusion and also by
electrophoresis. Dinitrocellulose membranes are particularly
favorable for use as blot membranes for mass spectrometric
analyses. These blot membranes are then used instead of the
microscopic tissue sections for the mass spectrometric
analysis.
A surface which is densely coated with an antibody can be used as
the copy medium in place of a blot membrane. This makes it possible
to extract various mutants, modification forms and also breakdown
forms of a single protein from the tissue and to analyze them with
spatial resolution, even if the protein is only present in the
tissue at a very low concentration. According to the invention, the
ratios of the mutants, modification forms and the breakdown forms
can be visualized as tissue state characteristics. It is
interesting and extremely informative, for example, to see how a
protein occurs mainly in singly phosphorylized form at some sites
in the tissue, while at other sites it is triply
phosphorylized.
The surface of the copy medium can also be coated with more than
one antibody, however, so that several proteins can be fished
simultaneously. If the fishing does not take place up to
saturation, the ratios of the proteins can again be represented as
characteristics which distinguish between different tissue
states.
The samples, either the prepared microscopic tissue sections or the
prepared copies, are then introduced into the mass spectrometer.
The mass spectrometric scans are then carried out using either the
raster scan method with a finely focused pulsed beam of laser light
or the scanning method with stigmatic imaging of the ions generated
over a large area.
The raster scan consists of a spot-by-spot acquisition of the mass
spectra, the finely-focused laser beam carrying out one
acquisition, or preferably many acquisitions, of mass spectra at
each spot of the tissue sample (or blot membrane sample). The mass
spectra of the same spot are added together in order to achieve a
higher dynamic range of measurement and also to improve the
statistics of the mass signals. The diameters of the "spots"
correspond roughly to the diameter of the laser focus, or to be
more precise, the diameter of the laser beam on the sample, which
can be adjusted by focusing. For the purposes of the raster it is
usually possible to set diameters of around 10 to 50 micrometers.
YAG lasers also permit focus diameters of less than one micrometer,
but no applications are known. The sum spectra are stored for every
spot of the raster. For a tissue area of one square millimeter
there can thus be 400 to 10,000 mass spectra, the normal figure
being around 1,000 to 2,000.
The raster is generally made up of measuring spots arranged in a
square, a parallelogram or a honeycomb shape, but it can, of
course, dispense with this type of pattern and following a specific
morphology of the sample, as would be helpful, for example, in the
case of an axon of a ganglion several millimeters long. The only
important thing is that the separations of the measuring spots are
adjusted to match the size of the area irradiated by the laser.
Ions generated from spots by MALDI can be analyzed with different
types of mass spectrometers. Time-of-flight mass spectrometers
(TOF-MS), with or without ion reflectors, are the usual method.
Time-of-flight mass spectrometers with orthogonal ion injection can
also be used. Ion traps and Fourier transform ion cyclotron
resonance (FT-ICR) are also being used increasingly.
The stigmatic image generates around 100 to 2,000 spatially
resolved mass signals from an irradiated surface of around 100 to
200 micrometers in diameter on a spatially-resolving detector.
Time-of-flight mass spectrometers with special ion focusing systems
for stigmatic imaging are used for this. The current art consists
in acquiring only the ion current signal for each laser pulse over
a narrow mass range, and masking out the remaining mass ranges,
since the time resolution of the detectors permits no other way of
measuring. For each of the other mass ranges the measurements must
be repeated. The mass ranges are chosen according to those masses
which have proven to be significant in previous analyses. It is,
however, to be expected that, in future, there will be cameras with
better time resolution. It will then be possible to scan the
complete mass spectra for a multitude of spots, although the
question of the mass resolution power is as yet unanswered. The
spatial resolution of this method promises to be better than that
of the raster scan. Relatively large areas are scanned one after
the other like a mosaic.
After the measurements, complete or partial mass spectra are then
available for each tissue spot. From these data it is possible to
calculate the characteristics which distinguish between different
tissue states for each spot, which is calculated as a mathematical
or logical expression from at least two mass signals (usually more)
of this tissue spot. This involves the use of the detailed
computational methods comprising algorithms and parameter sets
obtained in preliminary analyses of cohorts of samples. These
tissue state characteristics are then represented
graphically--preferably over the microscopic image.
A preferred representation of this tissue image consists in using
the microscopic image showing the structure of the tissue for the
color density (brightness of the image), and using the tissue state
characteristic for the color shade. It is then possible to
visualize healthy parts of the tissue in blue, diseased parts in
red, and the tissue structures in light-dark shades of the
respective color, for example. This type of representation produces
a higher resolution of the tissue state characteristics for the eye
than is provided by the measurements.
In a further embodiment of the invention, the computational methods
for the tissue state characteristics can also be developed, or at
least refined, using the mass spectra of the tissue itself (or of
two different pieces of tissue). In the tissue image on the screen
it is then also possible to indicate regions which are considered
to be healthy or diseased (FIG. 1A). From the mass spectra of these
regions it is then possible to develop computational methods for
distinguishing characteristics, independently on the computer using
predetermined guidelines. The computational method can follow a
previously determined algorithm, for example, where the parameter
set is merely optimized. A plurality of learning methods have been
elucidated for this type of optimization. It is also possible to
develop a new computational method according to a given development
scheme, independently on the computer. The improved or
newly-developed computational method is then applied to all spots
of the tissue, the calculated distinguishing characteristics being
represented in the tissue image (FIG. 1B).
It can also be interesting to compare more than two groups of
spectra with each other. In this case, several group-defining areas
are marked in the tissue section, or spread over several tissue
sections, and the characteristics are determined in such a way that
the groups can be distinguished from each other.
A further embodiment avoids the acquisition of spectra which are
not to be used analytically if the regions to be compared are
clearly recognizable. In the case of a spatially limited tumor, for
example, it can thus be sufficient to mark this and a
representative small part of the healthy tissue in the image of the
tissue section. Only these two areas, which are to be used for
determining the characteristics, are then actually measured.
In further embodiments, three-dimensional images of a tissue,
through several layers of microscopic tissue sections, for example,
can also be scanned and visualized according to the invention.
* * * * *